There is an important clinical need to develop functional imaging techniques that can quantify brain processes during human locomotion and relate them to body dynamics. Mobile brain imaging could assist with the diagnosis and treatment of patients with numerous movement disorders and neurological injuries. We propose that Independent Component Analysis of high-density electroencephalography (EEG) can quantify distinct brain processes involved in the control of human gait. Furthermore, we contend that electrocortical brain processes identified using Independent Component Analysis of EEG correlate with whole body dynamics. We will study healthy young subjects performing various locomotor tasks while we record movement kinematics and 256-channel EEG using active scalp electrodes. In Specific Aim 1, we will examine subjects walking at a range of speeds to determine if intra-stride patterns of activation and deactivation synchronized to the gait cycle are consistent across walking speeds. In Specific Aim 2, we will examine subjects performing passive recumbent stepping and active recumbent stepping to determine the relative effects of sensory feedback vs. motor feed forward commands with sensory feedback on electrocortical brain processes. We hypothesize that passive recumbent stepping will engage fewer electrocortical sources than active recumbent stepping. We will also compare active recumbent stepping with treadmill walking to determine the similarities between recumbent stepping and walking in activating cortical brain processes. In Specific Aim 3, we will examine subjects walking on a split-belt treadmill to quantify sensorimotor hemispheric independence using coherence. In Specific Aim 4, we will study subjects walking on a narrow treadmill-mounted balance beam to identify the electrocortical processes involved in maintaining and monitoring balance. The results from this study will advance our understanding of electrocortical dynamics related to the control of human walking, and will lead to new studies probing mechanisms of neurological gait impairments. The findings could also facilitate new brain- machine interface technologies for controlling robotic orthoses or prostheses.